As the first technique for visualizing inside the model, we focus on hidden layers of neural networks.
As an example, let us present in figure REF  the output of each convolutional layer of drag coefficient estimation [1]} and experimental data estimation [2]}.
These are canonical problem settings in fluid dynamics.
Since a force coefficient estimation plays a crucial role in fluid engineering, there is a demand in obtaining the force coefficients in less computational cost, i.e., without numerically solving the flow.
For instance, Zhang et al. [3]} utilized a CNN to estimate a force coefficient of an airfoil from flow characteristics and geometry information.
Similar studies can also be found in Refs. [1]}, [5]}.
In contrast, neural network-based experimental velocity data estimation addresses the various experimental constraints, e.g., obtaining denser flow motion [6]}, and inpainting the data missing region of an experimental image [2]}.
